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Update Chapter 04
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‎03.Lists_and_dictionaries.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"另一个常见方法是 **sort()**，它原地对列表按照默认递增顺序进行排序。可以手动指定 reverse 参数为True，使得按照降序进行排序。"
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"另一个常见方法是 **sort()**，它原地(in-place)对列表按照默认递增顺序进行排序。可以手动指定 reverse 参数为True，使得按照降序进行排序。"
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]
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},
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{

‎04.Tuples_and_files.ipynb‎

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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tuple"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type((1, 2))"
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]
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 2, 3, 4)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"(1, 2) + (3, 4) # Concatenation"
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]
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 2, 1, 2, 1, 2, 1, 2)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"(1, 2) * 4 # Repetition"
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]
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, (2, 3))"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"T = (1, 2, 3, 4) # Indexing, slicing\n",
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"T[0], T[1:3]"
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{
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"data": {
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"text/plain": [
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"b'\\x00\\x00\\x00\\x07spam\\x00\\x08\\x00\\x02'"
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"b'\\x00\\x00\\x00\\x12spam\\x00\\x08\\x00\\x02'"
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]
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},
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"execution_count": 41,
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"import struct\n",
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"\n",
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"F = open('data.bin', 'wb') # Open binary output file\n",
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"data = struct.pack('>i4s2h', 7, b'spam', 8, 2) # Make packed binary data\n",
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"data = struct.pack('>i4s2h', 18, b'spam', 8, 2) # Make packed binary data\n",
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"data"
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]
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},
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{
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"data": {
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"text/plain": [
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"b'\\x00\\x00\\x00\\x07spam\\x00\\x08\\x00\\x02'"
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"b'\\x00\\x00\\x00\\x12spam\\x00\\x08\\x00\\x02'"
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]
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},
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"execution_count": 43,
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{
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"data": {
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"text/plain": [
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"(7, b'spam', 8, 2)"
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"(18, b'spam', 8, 2)"
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]
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},
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"execution_count": 44,
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{
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"data": {
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"text/plain": [
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"(117440512, b'spam', 2048, 512)"
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"(301989888, b'spam', 2048, 512)"
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]
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},
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"execution_count": 45,
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"import numpy as np\n",
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"import struct\n",
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"\n",
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"image = np.zeros((256, 256), dtype=np.float)\n",
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"image = np.zeros((256, 256), dtype=np.single)\n",
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"for i in range(256):\n",
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" image[i] = np.arange(256)\n",
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"\n",
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"outputs": [
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{
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"data": {
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‎08.Numpy_and_matplotlib.ipynb‎

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@@ -3916,7 +3916,7 @@
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3919+
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‎17.Generative_adversarial_networks.ipynb‎

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"id": "981b2c97",
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"metadata": {},
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43-
"传统生成模型通常基于马尔可夫链、最大似然及近似推理，其代表模型有限制玻尔兹曼机(Restricted Boltzmann Machines, RBM）及其衍生模型如深度信念网络(Deep Belief Network， DBN)、深度波尔茨曼机(Deep Boltzmann Machines, DBM）、变分自动编码器(Variational Auto-Encoder, VAE)等，此类方法计算复杂且生成效果有限。"
43+
"传统生成模型通常基于马尔可夫链、最大似然及近似推理，其代表模型有限制玻尔兹曼机(Restricted Boltzmann Machines, RBM)及其衍生模型如深度信念网络(Deep Belief Network， DBN)、深度波尔茨曼机(Deep Boltzmann Machines, DBM)、变分自动编码器(Variational Auto-Encoder, VAE)等，此类方法计算复杂且生成效果有限。"
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},
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@@ -137,6 +137,19 @@
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